Learning to Fly Simple and Robust

Abstract

We report on new experiments with machine learning in the reconstruction of human sub-cognitive skill. The particular problem considered is to generate a clone of a human pilot performing a flying task on a simulated aircraft. The work presented here uses the human behaviour to create constraints for a search process that results in a controller -- pilot's clone. Experiments in this paper indicate that this approach, called ``indirect controllers'', results in pilot clones that are, in comparison with those obtained with traditional ``direct controllers'', simpler, more robust and easier to understand. An important feature of indirect controllers in this paper is the use of qualitative constraints.